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Counting the total number of missing values (NaNs) of a Pandas DataFrame

Pandas
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Handling Missing Values
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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tocTable of Contents
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Example

Consider the following DataFrame with some NaN values:

df = pd.DataFrame({"A":[np.nan,3,np.nan],"B":[4,np.nan,5],"C":[np.nan,7,8]}, index=["a","b","c"])
df
   A    B    C
a  NaN  4.0  NaN
b  3.0  NaN  7.0
c  NaN  5.0  8.0

Solution

To count the total number of NaN values in df:

df.isna().values.sum()
4

Explanation

Here, the df.isna() returns a DataFrame of booleans where True indicates entries that are NaN:

df.isna()
   A      B      C
a  True   False  True
b  False  True   False
c  True   False  False

Internally, True is represented by 1 while a False is represented by 0. Therefore, summing up all the values of df would then tell us how many NaN values there are.

The problem with the DataFrame's sum() method is that we can only compute the sum of each row or column of df. What we want to do instead is to compute the sum of all the values of the DataFrame.

We can do this by extracting the values of df as a NumPy array via the values property, and then calling its sum() method:

df.isna().values.sum()
4
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Published by Isshin Inada
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